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China is the top emitter of greenhouse gases in the world. Pulp and paper industry is one of eight the strictly regulated industries about carbon emission in China. However, estimates of carbon emissions from China's pulp and paper industry have been suffered from remarkable uncertainty. It is summarized that is aroused by the inaccuracy of factors evaluating energy consumption, and the huge gap between the calculation standards for assessing energy consumption in the production process and the actual level recorded in the factories. This paper re-evaluated the carbon emission of China's pulp and paper industry from the perspective of life cycle assessment, and applied more accurate emission factors, taking account of the actual energy consumption of the production process in factories, to estimate the actual carbon emissions in maximum. It is found that the national standards about the energy consumption quota of China's pulp and paper industry is no longer applicable, and the actual energy consumption regulated in the production process is far better than the advanced level given by the national standards. The greenhouse gas emission have been seriously overestimated approximately 12.00%–56.17% by international organizations. • Life Cycle Analysis of the China's pulp and paper Industry is conducted thoroughly. • The pulp flow of China's pulp and paper Industry is analyzed. • Energy regulated in factory is far better than the national standards' required. • Carbon emissions of China's pulp and paper Industry are overestimated. [ABSTRACT FROM AUTHOR]

To reduce energy use and achieve a low carbon production in pulp and paper industry (PPI), it is essential to improve energy efficiency in pulp and paper production. In this study, the energy savings from the PPI were estimated based on a techno-economic analysis by EAGER tool for PPI at the factory level. The structure and implement procedure were introduced initially. Two typical pulp and paper mills from China were determined to estimate the impact of selected energy efficiency technologies (EETs) on energy savings as the cases in the base year. One Factory (A) only produced paper products (i.e. corrugated paper and Kraft linerboard) while the other Factory (B) also produced market pulp besides sack paper. In totally 22 selected EETs were applied to assess their saving potential in Factory A, and 29 measures were applied in Factory B. The results show that 2290 TJ and 1179 TJ of final energy could be saved annually in Factory A and B, corresponding to reduction of 29.4% and 13.6% total energy consumption respectively for the case factories. The CO 2 reduction potential was 32.3% for Factory A compared with that of 16.6% in Factory B. It suggests that the pulp and paper mills should encouraged to use this method estimating energy saving potential, and improve their energy efficiency with suggested measures in order to reduce energy consumption and related carbon emissions further in the coming years. • Energy savings of pulp and paper production were estimated at the factory level. • A techno-economic evaluation tool was applied to access the energy savings and carbon emission reductions. • Two typical pulp and paper factories from China were investigated to estimate their energy savings in the base year. • It is useful to assess energy savings and select feasible EETs in achieving energy saving and emission reduction targets. [ABSTRACT FROM AUTHOR]

In order to guide the operation of the actual cascade system, this paper takes the cascade system as the research object and compares the use of six refrigerant pairs. Unlike the previous single-objective optimization of refrigeration system carbon emissions and other indicators, this research uses a multi-objective optimization algorithm, that is, we consider the cascade system's exergy efficiency and carbon emissions at the same time. We reduce carbon emissions while improving the system's exergy efficiency, and finally find out the specific operating conditions under which the system can realize low-carbon and high-efficiency operation, which further promotes the process of achieving energy saving and emission reduction in factories. The optimization results show that among the six refrigerant pairs, the use of R744-R134a and R744-R290 refrigerant pairs can make the system operate better, but considering the high global warming potential (GWP) of R134a refrigerant, the use of R744-R290 combination is more recommended. [Display omitted] [ABSTRACT FROM AUTHOR]

The mechanism of the environmental regulations affecting carbon emissions is complex, and for many years it has been a hot research topic for researchers. However, few of the previous studies have focused on whether or not environmental regulations can potentially influence carbon emissions through technical efficiency. Therefore, the present study chose to investigate the mediation effects of the technical efficiencies between environmental regulations and carbon emissions using the provincial panel data of energy-intensive industries for the period ranging from 2005 to 2015. The results were as follows: (i) In regard to the entire group of energy-intensive industries, it was found that environmental regulations could not only potentially directly reduce carbon emissions, but also indirectly reduce carbon emissions through technical efficiency. In other words, technical efficiency had played a partial mediation role between the environmental regulations and the carbon emissions; (ii) It was observed that for the different subdivided energy-intensive industries, the different levels of technical efficiency had shown various mediation effects. For example, the mediation effects of technical efficiency in the petroleum processing and coking (PPC) industry; papermaking and paper products (PPP) industry, and raw chemical materials and chemical products (RCMCP) industry, were found to be similar to those of the whole energy-intensive industries. Furthermore, the "Porter Hypothesis" had been verified in the nonmetal mineral products (NMP) industry. However, there were no mediation effects observed in the smelting and pressing of ferrous metals (SPFM) and smelting and pressing of nonferrous metals (SPNM) industries; (iii) In this study, in accordance with the regression results of the double-threshold models, it was found that for the energy-intensive industries as a whole, the carbon emission reduction effects of environmental regulations had become stronger with the increases in technical efficiency. For the PPP and RCMCP industries, there was an inverted U-shaped relationship observed between environmental regulations and carbon emissions when the technical efficiency was taken as the threshold variable. Therefore, based on the research conclusions achieved in this study, some policy recommendations were put forward. • Environmental regulation reduces carbon emission of the energy intensive industries of China. • Technical efficiency plays a partial mediation effect between environmental regulation and carbon emission. • The mediation effects of technical efficiency differ in the subdivided energy-intensive industries. • The "Porter hypothesis" is verified in Nonmetal Mineral Products (NMP) industry. [ABSTRACT FROM AUTHOR]

Against the backdrop of climate change becoming a global hot topic, global carbon emissions continue to grow. As the world's largest developing country, China is facing increasingly severe pressure to reduce emissions in its development. At the same time, in the current context of low-carbon transformation, the country has also begun to vigorously promote the carbon emission trading market. In the development process of China's carbon emissions trading market, there are still problems such as uneven data quality, insufficient data accuracy and standardization, which affect the normal operation of the carbon emissions trading market. Therefore, while the carbon emissions trading market is rapidly developing, it is necessary to establish a comprehensive carbon emissions data management system. This article analyzes the problems and future development directions of China's carbon emission data management system, and designs an overall design scheme for a carbon emission data management system based on multi-source data fusion. Through comparative experiments of UOS (ultimate Oscillator) indicators, it was found that the data transmission capacity of the carbon emission data management system designed in this paper with multi-source data can gradually improve the UOS value through continuous use. At the end of the experiment, it obtained a UOS value of up to 91.23%, far exceeding the efficiency of traditional data transmission. [ABSTRACT FROM AUTHOR]

In this paper, we study whether a firm's carbon risk exposure plays a role in the relationship between dividend announcements and stock returns. Our results show that when investors hold disproportionately high carbon emitters with associated increased carbon risk, a positive relationship exists between a firm's carbon emissions and the association between the stock returns and dividend payment. If investors hold disproportionately high carbon emitters with the associated increased carbon risk stocks, the stock market reacts less positively (more negatively) to dividend increase (decrease) announcements. At the same time, if firms under-price their carbon risk, the stock market reacts less positively (more negatively) to dividend increase (decrease) announcements. [ABSTRACT FROM AUTHOR]

This paper examines the impact of green finance on environmental performance in developing countries. Using panel cointegration analysis and panel vector error correction model (VECM) to a sample of 76 developing economies from 2010 to 2019, this study confirms that carbon emissions (CO 2) is cointegrated with green finance, real gross domestic product, urbanization, and renewable energy under different specifications and therefore we estimate the long-run relationships among these variables. Specifically, both green finance and renewable energy are found to exert significant and inhibitory effects on CO 2 emissions, reaffirming the necessity to promote green finance and renewable energy. Results from the paper also document the existence of the Environmental Kuznets Curve and the increasing effects of urbanization on CO 2 emissions. Several policy implications and recommendations are drawn from this study. • We examine impact of green finance on environmental performance. • 76 developing economies from 2010 to 2019 are considered. • Green finance and renewable energy exert inhibitory effects on CO 2 emissions. • Results support the existence of the Environmental Kuznets Curve. [ABSTRACT FROM AUTHOR]

To abate global carbon emissions, there has been an unprecedented push for carbon transparency in the manufacturing industry. However, with products becoming increasingly complex, they contain sub-assemblies with components that have sub-components supplied by different manufacturers. This creates a multi-tier supply chain that complicates the propagation of sustainability information necessary to compute the product's carbon emission due to the lack of oversight and potential loss of information from manufacturers in the multi-tier supply chain. Blockchain technology, characterized by its immutability, visibility, and traceability, is a promising medium to propagate sustainability information. Despite the fact that many papers have proposed a system that is viable for products with a multi-tier supply chain in the literature, we have not seen its proof-of-concept yet. This work first proposes a blockchain-enabled system that enables the propagation of environmental sustainability information in a multi-tier supply chain. This paper describes the architecture of the proposed blockchain system and the data structure of each block in the blockchain. Based on the defined architecture, we implemented the proposed blockchain system with Ethereum and tested it using the pseudo-multi-tier supply chain of a torchlight. [ABSTRACT FROM AUTHOR]

With the increasing concentration of greenhouse gases, carbon peaking and carbon neutrality have gradually become a global issue. As the last link of e-commerce logistics, the last mile delivery should not only focus on customer satisfaction and enterprise cost, but also reduce carbon emission and promote green development. In order to solve the last mile delivery problem, this paper builds the last mile delivery model for two common delivery vehicles, considering the impact of transportation cost, carbon emission and customer satisfaction on the last mile delivery path planning. The last mile delivery is an NP-hard problem, a novel Discrete Marine Predators Algorithm(DMPA) is proposed to solve the problem, which combines neighborhood search strategy and RS hybrid operator. Six different test examples are generated based on Solomon for simulation experiments, and the experimental results verify that the proposed algorithm is obviously superior to the marine predators algorithm, simulated annealing algorithm and genetic algorithm. DMPA obtains significantly higher customer satisfaction than other algorithms. The customer satisfaction of C101 and C201 is as high as 0.99, which is close to the optimal value of 1. DMPA can achieve lower transportation costs and less carbon emissions compared with other algorithms. DMPA reduces the transportation cost by at least 61.12$ and the carbon emission by at least 1033.64 kg for C101 by minivan delivery. DMPA reduces the transportation cost by at least 28.89$ and the carbon emission by at least 210.9 kg for C101 by electric tricycle delivery. It can effectively reduce carbon emission, lower transportation cost, improve service level, and promote the integration of the dual carbon target with the internal requirements of national development. • Carbon emission, transportation cost and service level are considered in the last mile delivery. • This paper builds the problem model for two delivery vehicles. • An improved discrete marine predators algorithm is proposed. • The proposed algorithm is superior to other well-known algorithms. [ABSTRACT FROM AUTHOR]

[Display omitted] • Carbon emission is creating a distressing situation and requires substantial solutions. • Economic complexity and FDI reduced carbon emissions. • Green technology and Renewable energy correlated negatively with carbon emissions. • Positive shocks in ECI, FDI, GRT, and REU reduce COE. • Negative shocks in GRT and REU can lead to increased pollution in the long run. All economies are concerned about rising carbon emissions, which contribute to environmental degradation. The current paper formulates a novel framework to scrutinize the impacts of shocks in economic complexity, FDI, environmental technology, and renewable energy on carbon emission in the leading clean energy investment countries, spanning the period from 1995 to 2020. In spite of the constraint for better environmental defence and the realization of the Sustainable Development Goals (SDGs), this paper introduces an empirical approach utilizing the Panel NARDL methodology to investigate the asymmetrical connections between carbon emissions and relevant exogenous factors. Furthermore, we utilize additional techniques, namely AMG and CCEMG, to enhance the robustness of our findings. Our empirical findings reveal that positive shocks in economic complexity, FDI, environmental technology, and renewable energy reduce carbon emissions while negative shocks may result to elevated pollution levels in the long-run. However, adverse shocks in economic complexity and FDI cause increased pollution in the long run. Likewise, the short-run coefficient signs are also similar to the long-run coefficient signs but different in significance level and magnitude. This has paved the way for a well-designed policy for leading clean-energy investment countries should focus on structural change, FDI, technology and renewable energy consumption. [ABSTRACT FROM AUTHOR]

• An improved singular spectrum decomposition is proposed. • High complexity components are secondarily decomposed to reduce its complexity. • An improved prediction model of optimization algorithm is proposed. • Intelligent weighting strategy is introduced to overcome traditional weighting problem. • A combined prediction model for carbon emission is proposed. Accurate prediction of carbon emission is critical for the development of low-carbon economy. However, most carbon emission prediction studies use a single model with low prediction accuracy, and do not consider the instability of carbon emission. Therefore, this paper proposes a combined prediction model of carbon emission. Firstly, the original data is decomposed by singular spectrum decomposition to obtain a limited amount of singular spectrum components. Secondly, high complexity components are secondarily decomposed by variational mode decomposition. Then, chameleon swarm algorithm and carnivorous plant algorithm are used to train the regularization coefficients and kernel parameters of kernel extreme learning machine and least squares support vector machine respectively, and the trained model is used to predict the decomposition components. Finally, induced ordered weighted averaging operator is used to calculate the weight of single model, and error correction is introduced to further promote the prediction accuracy. The carbon emission data of China and the United States is used to make a prediction experiment. The results indicate that the proposed model is superior to other comparative models in different indexes, which provides a new idea for carbon emission prediction. [ABSTRACT FROM AUTHOR]

The current energy structure of most economies must be upgraded to a renewable energy source (RES) to help mitigate ecological dilapidation and achieve sustainable development goals. The transition to RES has recently received attention from energy experts, international bodies and environmental scientists. However, there is contrasting evidence on what factors affect the evolution and deployment of RES, especially from the perspective of emerging African economies. Hence, the current analysis sought to explore the influence of economic development (ECD), carbon emission (CEM), human capital (HUC), financial development (FID), and the country's risk factors, such as political risk (POR) and institutional quality (INQ) on the development of RES. The research employed a panel dataset from 1990 to 2020 and used the newest estimation approach of Cross-Sectional Augmented autoregressive Distributed Lag (CS-ARDL) to establish the long-run connection among the variables. The study confirmed the prevalence of the "energy-led ECD hypothesis". In addition, the empirical findings indicated that RES deployment could be facilitated through ECD, CEM, FID and HUC. Moreover, the country's risk factors, which include POR and INQ, had an inverse connection with the deployment of RES. Lastly, there is a unidirectional causality between CEM, FID, HUC, INQ and RES, while a directional causality exists between ECD, POR and RES. The paper highlights a variety of policy angles to help with the expansion of RES in emerging regions. • The study confirmed the prevalence of the "energy-led ECD hypothesis. • RES deployment could be facilitated through ECD, CEM, FID and HUC. • POR and INQ, had an inverse connection with the deployment of RES. • A directional causality exists between ECD, POR and RES. [ABSTRACT FROM AUTHOR]

The development of ultra-high performance concrete (UHPC) with lower carbon emission has conducted by many researchers. However, there is a lack of quantitative comparison of carbon reduction efficiency, such as carbon emission and energy consumption values, of UHPC materials with various components. This paper reviews the trends toward lower-carbon UHPC based on Life Cycle Assessment related to the used materials, especially different binders, aggregates, and steel fibers. The environmental indicators, including embodied carbon emissions (EC), embodied energy consumption (EE), and embodied CO 2 index (CI) value, of UHPC mixtures in published paper are discussed. Since compressive strength is an important indicator, the minimum value of compressive strength of UHPC should be greater than 120 MPa in the assessment process. Moreover, the effectiveness of using various low-carbon alternatives is discussed and then ranked according to the reduction degrees of EC and EE. Results found that ultra-high performance geopolymer concrete (UHPGC) shows the lowest EC and EE and the highest CI. This indicates that UHPGC has decreased carbon emissions without compromising compressive strength. The use of supplementary cementitious materials and recycled powder obtained from waste aggregates to substitute cement is another effective way to reduce EE and EC. However, using waste aggregates or waste fibers to replace conventional aggregates or steel fibers is less efficient than cement replacement with supplementary cementitious materials (SCMs). Especially, the use of waste aggregate leads to negligible reductions in EC and EE. The study seeks to find out the most promising route for low-carbon UHPC in the circular economy through identifying low-carbon alternatives, which has great significance in designing low-carbon UHPC. • Multiple strategies to develop lower-carbon UHPC are reviewed. • The environmental impacts of different UHPC are systematically calculated based on LCA at a material level. • These strategies are discussed and ranked based on performances and environmental impacts. • The most effective strategy for developing lower-carbon UHPC is indicated. • Perspective for how to develop lower-carbon UHPC is given. [ABSTRACT FROM AUTHOR]

• A multi-objective model is proposed, which contains both the cost and carbon emission objectives. The sizing of the grid-connected converter is included. The carbon emission of the power from the grid is considered. The model can comprehensively describe the requirements of LVDC systems in the economy and environmental protection. • This paper considers the effect of EVs on the sizing of LVDC systems. Both the charging and discharging of EVs are fully used to reach better sizing and operation for the system. • A minimum fuzzy carbon emission method (MFCEM) is given to transform the multi-objective into a single objective. Compared with the linear weighting method, MFCEM is a useful method for finding a point on the Pareto front that balances cost and carbon emissions. Grid-connected low-voltage direct current (LVDC) systems offer a promising solution for achieving low-carbon buildings. However, a new challenge arises in sizing grid-connected LVDC systems, which is to fully utilize distributed renewable energy, energy storage, and load demand response to minimize both the economy and carbon emissions. To address this gap, this paper studies the sizing of grid-connected LVDC systems, considering the grid-connected converter, photovoltaic (PV), battery, and demand response of electric vehicles (EVs). Firstly, two objective functions are proposed, including the economy and carbon emissions. Secondly, constraints for sizing and operation are presented, with particular consideration given to the discharging of EVs and the corresponding cost of cycle-life loss. Finally, a minimum fuzzy carbon emission method (MFCEM) is proposed to solve the model, which can adaptively and objectively transform the multi-objective into a single objective. According to the case study, the proposed model comprehensively considers the cost and carbon emissions of grid-connected LVDC systems, and its feasibility and validity are verified. Compared with the linear weighting method, MFCEM is a useful method for finding a point on the Pareto front that balances cost and carbon emissions. [ABSTRACT FROM AUTHOR]

This paper studies the biomass–coal co-firing (BCC) production planning problem under the carbon emission quota allocation (CEQA) mechanism. However, carbon emission parameters are uncertain due to many factors, such as the type of power units and coal quality. To address this challenge issue, this paper proposes a new globalized robust bi-level optimization model, where the uncertain parameters are characterized by a pair of uncertainty sets. In the proposed model, the upper government as the leader decides the CEQA mechanism, while the lower power plants as the followers develop the production planning according to the given CEQA mechanism. Moreover, based on the Lagrange duality theory and Karush–Kuhn–Tucker (KKT) conditions, the proposed model is equivalently converted into a computationally tractable single-level model. Finally, a practical case study in Shandong Province demonstrates that compared with the nominal bi-level and robust bi-level models, the proposed optimization model can not only effectively resist parameter uncertainty, but also be less conservative. [Display omitted] • A bi-level GRO model for BCC production planning problem under CEQA mechanism is proposed. • The uncertain carbon emission parameters are characterized by a pair of uncertainty sets. • The established globalized robust counterpart is transformed equivalently into a finite convex system. • The effectiveness of the developed GRO approach is verified via a case study. [ABSTRACT FROM AUTHOR]

With the development of multi-energy systems and the urgent demand for low-carbon energy provision, the regional integrated energy system (IES) is considered an efficient paradigm to improve energy efficiency and carbon emission reduction. The optimal capacity planning of different IES components is considered a non-trivial task due to the uncertainties of renewable power generation and various demands (e.g., electricity, heating and cooling) as well as the carbon emission constraints. This paper addresses this challenge and presents a scenario-based robust optimal planning solution for the regional low-carbon IES fully considering the economic cost, carbon emission and energy supply reliability. The operational uncertainties of different forms of energy sources and demands are characterized by a controllable generative adversarial network (GAN). The proposed method is extensively assessed based on an IES case study in China through a comparative analysis, the numerical results show that compared with the traditional planning method, the proposed capacity planning solution can reduce the total cost by 4.24% and the carbon emissions by 42.61%, the effectiveness and benefits of the planning solution have been effectively confirmed. • A scenario-based robust capacity planning solution of IES with carbon emission consideration is proposed. • A controllable GAN-based scenario generation is adopted for operational uncertainty characterization. • The proposed solution can reduce both the total cost and carbon emissions. [ABSTRACT FROM AUTHOR]

Under the background of "One national network", the balance between the natural gas supply side and the demand side is prominent. This paper proposes a supply-demand balance method of natural gas pipeline network coupled with the hydraulic and thermal characteristics, and decides the transportation scheme through flow rate allocation. This paper designs a two-stage relaxation optimization algorithm, coupled the thermal process into the hydraulic calculation to improve the hydraulic calculation accuracy, making the natural gas transportation scheme more applicable to engineering. The result shows the feasibility of transportation decision-making and the accuracy of hydro thermal calculation of the proposed model through a real case. The relative error of thermal calculation and TGNET is below 2%, and the hydraulic calculation error of coupled thermal can also be within 10%. After optimization, the carbon emissions decrease from 418,100 tons to 260,000 tons, reducing by about 37.81%. This study also analyzes the impact of hydraulic calculation on carbon emissions calculation, and further illustrates the necessity of coupled thermal characteristics in transportation scheme. This study can provide decision support for the transportation operation and help the natural gas pipeline network to meet the dual carbon requirements and achieve sustainable development. • A flow rate allocation method combining operation process is proposed. • A convex relaxation optimization algorithm is designed for energy consumption calculation. • Two stage relaxation iteration algorithm is designed to couple thermal and hydraulic characteristics. • Carbon emission objective is added to the optimization model for environmental benefit. • An actual case is used to prove the practicability of the method. [ABSTRACT FROM AUTHOR]

The significant environmental impact of buildings during their lifecycle has made it a critical area to address the global climate crisis. However, due to the involvement of multiple industrial sectors, a large number of related parties, and the complexity of environmental information flows in the whole lifecycle of buildings, the effectiveness of the traditional centralized management approach is limited, and it is necessary to adopt more advanced management technology tools. With its decentralized, transparent, and secure characteristics, blockchain technology has triggered various applications in several industries, as well as in the field of building environmental sustainability. Using bibliometric and content analysis, this paper comprehensively reviews the current state of research on blockchain technology in the field of building environmental sustainability. A total of 128 articles were reviewed, revealing that blockchain technology has been used to address various types of environmental problems in multiple stages of the building lifecycle, including building material production, construction, operation, and demolition phases, etc. It is shown that blockchain technology can already effectively improve the use of renewable energy and reduce overall energy consumption during the building operation phase, while the great potential for solving other environmental issues, such as the management of building embodied carbon emissions, needs to be further explored. This paper also explores potential research opportunities and future directions based on current gaps and limitations, presenting several critical topics from the perspective of building lifecycle and practice support. This review helps related researchers gain insights into the research frontier and future direction. • Blockchain technology improves environmental performance across building lifecycle. • A comprehensive research status is shown through bibliometric and content analysis. • Main environmental issues and highlighted blockchain technologies are discussed. • Further blockchain applications are urgently needed for building carbon emissions. • Potential research opportunities and priorities for the future are presented. [ABSTRACT FROM AUTHOR]

With the rapid development of China's economy, the contradiction of the mismatch between municipal solid waste disposal capacity and its growth has become increasingly prominent, and the need to develop alternative energy sources has become increasingly urgent. Municipal solid waste incineration plays an important role in waste management. In this study, the Grey Relation Analysis (GRA) and Long Short Term Memory (LSTM) prediction model is constructed to achieve effective prediction of municipal solid waste generation by taking 11 influencing factors of municipal solid waste generation as input indicators in Tianjin. Meanwhile, this study predicts the power generation from municipal solid waste incineration and carbon emissions in Tianjin based on the prediction results. The experimental results show that the mean absolute percentage error (MAPE) value of the combined GRA-LSTM model established in this study is 6.759 %, and this model outperforms the other five structural methods in terms of prediction performance. By 2025, the amount of MSW in Tianjin will reach 3.74 million metric tons, the power generation through incineration will be 20.02–39.47 GWh, and the CO 2 emission through incineration will be about 1.14 million metric tons. Finally, to cope with the growth of municipal solid waste generation and unbalanced power supply structure in the country's mega-cities, this paper puts forward relevant suggestions for achieving sustainable urban development. [ABSTRACT FROM AUTHOR]

The long-distance cold chain transportation of fresh products from the place of origin to sale is costly due to the high probability of spoilage and high carbon emissions. The potential factors for the high cost of long-distance cold chain transportation are the spoilage of fresh goods caused by transportation vibration and the carbon emission caused by refrigerant use. How to minimize the impact of these factors through optimal transportation route planning is the current problem to be solved. Firstly, a longdistance cold chain logistics route planning model including goods spoilage cost and carbon emission cost is developed, considering the goods spoilage cost caused by transportation vibration and the carbon emission cost generated by refrigerant as well as several other factors. Secondly, an improved ant colony algorithm is used to solve the optimal solution of the model, and the algorithm is redesigned for the characteristics of the model. Thirdly, the pheromone update mechanism in the ant colony algorithm is improved by adding pheromone coefficients to accelerate the convergence speed of the algorithm. Finally, the validity of the model is verified by an arithmetic example. The model and method proposed in this paper help to reduce the cost in the process of long-distance cold chain transportation and bring practical application value. [ABSTRACT FROM AUTHOR]

• A novel in-rack servers and PEMFC coupling scheme is proposed. • A rack level prototype is built. • The coupled electric performance and thermal performance are tested. • The prototype can provide hot water up to 61.1 ℃. • The zero-carbon emission operation of the prototype is ensured. The combination of data center and PEMFC has been a promising data center zero-carbon emission solution. However, the relevant experimental studies are very rare and most work only solved the simple electrical coupling. This paper proposed a new in-rack data center servers and PEMFC coupling design scheme considering both the electrical coupling and thermal management coupling of the servers and PEMFC, and built a cabinet prototype for the first time. The complete multi-condition continuous experiment on the PEMFC system fully demonstrates the servers power supply stability of the prototype with the fast tested power response capability of 4.9 kW in 0.1 s. The experiments under rated condition (when the servers power is 5 kW) were conducted and the carbon emission level and efficiency performance of the prototype are analyzed. Under the rated condition, the highest prototype CHP efficiency is 64.1 % while the prototype can provide 41.6 ℃ hot water with 3.04 L·min-1 and the lowest prototype CHP efficiency is 59.8 % while the prototype can provide 61.1 ℃ hot water with 1.33 L·min-1. The prototype maintains the zero-carbon (or negative carbon) emission of servers. The average operation carbon emission rate is -35 g·h-1. Compared with the full grid supporting data center, the power average prototype operation carbon reduction rate is 0.13 g·s-1·kW-1. The application potential of the proposed data center and PEMFC coupling scheme is demonstrated with the prototype experiment results from the advantage of carbon emission and heat recovery potential. [ABSTRACT FROM AUTHOR]

In contemporary times, where most academic research mainly focuses on the factors of economic and environmental sustainability and emissions reduction. Yet, very little attention has been paid to the identification of the factors of renewable energy, which requires appropriate policy-level attention. Consequently, this research investigated two developed economies, i.e., Germany and the USA, from 1991 to 2021 where, the objective of the study includes using novel and robust empirical methods to test the causal relationship between renewable energy and CO2 emissions, economic growth, technological innovations and oil prices. Using the normality and unit root estimators, this study observed that non-normal data distribution, yet all the variables are stationary. Using time-series and panel cointegration tests, the results validate the cointegration between economic growth, oil prices, carbon emission, technological innovation, and renewable energy adoption in the United States whereas Germany does not show cointegration between the variables. This study employ 's the Morlet-Wavelet approaches and key findings show that all these variables have a significant role in improving renewable energy adoption in both the region. Furthermore, results show a unidirectional and bidirectional causal association between the variables via the panel-stacked Granger causality test. This study recommends effective policy ramifications concerning improved investment in technological innovation, improved low-carbon production, and diverting economic growth to renewable energy transition. Use of improved new time-series method of wavelet coherence show the key contribution in this paper with new evidence of time frequency analysis on how external variables affect renewable energy consumption in developed countries of US and Germany. The objective includes understanding the effects of CO2 emissions, economic growth, technological innovations and oil prices on renewable energy which would give evidence to policy makers and environmentalists on how developed countries should improve clean energy adoption. [ABSTRACT FROM AUTHOR]

[Display omitted] • Performance of medium-deep geothermal heat exchanger was studied. • Logging data and tested heat exchanger performance were presented. • Algorithm was validated with less than 10.65% relative error after stabilization. • Heat exchange rate varies from 169 kW to 517 kW under different operating parameters. • Lifecycle carbon emission changes from 18.5 kg·kW−1 to 9048.8 kg·kW−1. Geothermal energy is one of the most competitive renewable energies and the medium-deep geothermal energy has been broadly adopted for building heating. This paper reported a long-term performance study on the medium-deep ground heat exchangers (GHEs) based on a project in Xixian New Area, China. The logging data of a 2,500 m well and the tested inlet/outlet temperature were presented, before a numerical simulation algorithm was validated and adopted to predict the GHE long-term performance affected by operating parameters. It turns out that the algorithm can forecast the performance with a maximum relative error of less than 10.65 % at the stabilization stage. Under long-term operation, the average heat exchange rate becomes stable since the eighth year. The heat exchange rate varies from 169 kW to 517 kW through the life cycle when changing flow rate and daily operating time. The carbon emission needed for per unit heat extraction ranges from 18.5 kg·kW−1 to 9,048.8 kg·kW−1. Properly adjusting the operating parameters according to the building load will be beneficial to reducing carbon emissions and improving heat extraction efficiency. It is expected that the results will be conducive to the high-efficiency and low-carbon usage of geothermal energy. [ABSTRACT FROM AUTHOR]

When powered by electricity, Connected and Autonomous Vehicles (CAVs), an emerging mode of transportation, possess the capacity to reduce exhaust emissions greatly. However, accurately measuring carbon emissions in urban transportation remains a challenge, especially considering emissions from electricity generation and gasoline consumption. This paper proposes an innovative method for calculating CAVs' carbon emissions distribution, utilizing both renewable and non-renewable energy. The study employs SUMO, an agent-based simulation platform, to develop an intelligent driver model and cooperative adaptive cruise control modules, tracking vehicle movement behavior across various vehicle types, including Gasoline Vehicles (GVs), Electric Vehicles (EVs), Human-Driven Vehicles (HDVs), and CAVs. Subsequently, a lifecycle electric carbon emission model is constructed, integrating the energy consumption model of EVs with carbon emission factors of renewable and non-renewable energy. Visualization models are then developed to clarify the carbon emission distribution within the traffic network. A case study conducted in Suzhou, China validates the model, analyzing the spatiotemporal distribution of carbon emissions. Results show EVs can reduce carbon emissions by 70%–90 % compared to GVs on urban roads during rush hour, while CAVs can further reduce emissions by 35%–50 % compared to HDVs. Additionally, carbon emissions from non-renewable energy sources were found to exceed those from renewable sources. [ABSTRACT FROM AUTHOR]

This paper addresses the sustainable design of hub networks under uncertainty in the context of less-than-truckload transportation, taking into account factors related to carbon pricing. The problem is modeled to maximize profits in a stochastic demand environment, where a portion of the demand may remain unserved depending on the trade-off between profits, costs, and carbon emissions. The model explicitly incorporates a carbon tax into the objective function, along with transportation and hub operation costs. To ensure compliance with the carbon cap, a constraint is incorporated to limit the emissions across the entire transportation network. The carbon emission on each arc of the network is modeled using a generic convex function that depends on the total demand routed on the arc which is then approximated by a piecewise linear function to derive a mixed-integer stochastic formulation. A Benders-decomposition-based algorithm coupled with a sample average approximation scheme is developed to solve the stochastic model. The algorithm is enhanced with acceleration techniques to solve large-scale instances. Extensive computational experiments are conducted to evaluate the efficiency of the proposed algorithm and also to analyze the impact of incorporating carbon pricing factors on optimal hub networks. Computational results provide insights into sustainable hub network designs. • Modeled a sustainable hub network design problem under demand uncertainty. • Incorporated carbon pricing factors including a carbon tax and carbon cap. • Developed an exact algorithm and solved realistic-size instances. • Analyzed the impact of carbon pricing factors on the resulting hub networks. [ABSTRACT FROM AUTHOR]

The current research investigating the effectiveness of policy-driven transformation in resource-based cities has focused mainly on city-level impacts and has overlooked firm-level evidence. This paper filled this gap by focusing on its impact on firm-level carbon emissions. Drawing on the quasi-natural experiment of China's reform of resource-based city transformation, we utilize a difference-in-differences (DID) model based on nationwide firm-level tax survey data from 2009 to 2016. The findings indicate a noteworthy reduction in firms' carbon emissions due to the reform, particularly in growing-type resource-based cities and within the manufacturing industry. The empirical results provide evidence that the implementation of the "Plan" has effectively enhanced the level of urban resilience. Moreover, the findings demonstrate stronger effects on large firms, publicly owned firms, and firms in municipal districts. • Resource-based city transformation is effective at mitigating firm carbon emissions and enhancing urban resilience. • Different development stages exhibit heterogeneity in the effectiveness of firm emissions reductions. • Sector- and firm-specific characteristics play a significant role in reduction in firms' carbon emissions. [ABSTRACT FROM AUTHOR]

This paper forecasts the growth of freight transport emissions in India's trucking sector until 2050. We use the ARIMAX time series model to project the commercial vehicle population under various economic growth scenarios. CO 2 emissions are estimated using emission factors, distance traveled, and the projected commercial vehicle population. We use the validated forecasting model to investigate how emissions can be curbed through vehicle scrappage policies or fleet electrification to realign with the 2015 Paris Agreement's targets. Compared to the business-as-usual (BAU) scenario, 10-year and 15-year vehicle scrappage policies are estimated to reduce freight emissions by 6–11% and 1–2%, respectively. Fleet electrification has more pronounced effects as it results in a 35% and 70% reduction in carbon emissions for heavy and light goods vehicles in 2050, respectively, compared to the BAU scenario. These findings provide crucial insights for devising policies to mitigate greenhouse gas emissions in India's trucking industry. [ABSTRACT FROM AUTHOR]

The high proportion and volatility of renewable energy pose a significant challenge to efficient collaboration between photovoltaic/thermal and wind power in multi-energy systems. Therefore, this paper proposes a Regional Multi-Energy System (RMES) based on Medium-High Temperature Solar Thermal (MHTST) technology. The novelty of the solution integrates dispatchable photovoltaic and photothermal conversion pathways, facilitating flexible and controllable utilization of solar energy via installed Thermal Energy Storage (TES). A multi-objective optimization model is developed to ensure the optimal operation of the proposed system under typical seasonal scenarios. The method that combines Branch-and-Reduce with Multi-Learning-Task is used to handle the formulated Mixed Integer Nonlinear Programming (MINLP) and swiftly identify optimal Pareto solutions. Case studies are performed over the improved united communities of the Test Region for Energy Transition and Comprehensive Reform in Taiyuan, China. Compared with the conventional photovoltaic technology, the simulation results show that the proposed solution has superior performance in both winter-heating and summer-cooling operations across 24-hour scheduling periods: operating costs reduced by 12.5% and 18.7%, carbon emissions decreased by 7.4% and 21.4%, with local solar power accommodation increased by 263.89kW and 487.47kW, respectively. • A novel regional multi-energy system integrating medium-high temperature solar thermal is proposed. • The model solution combined branch-and-reduce with multi-learning-task is proposed. • The proposed strategy offers optimal energy schedules while reflecting renewable electricity trading. • System performances of photovoltaic and medium-high temperature solar thermal systems are compared. [ABSTRACT FROM AUTHOR]

As China becomes the world's largest country for carbon emissions, it becomes one of China's major tasks to seek a path of coordinated development between the environment and the economy. Decoupling analysis is a significant method for analysing the relationship between economic growth and carbon emissions. This paper studies the changes and causes of decoupling index at two levels. At the national level, this paper decomposes the decoupling of carbon emissions from GDP into three parts. Then, the Laspeyers method is adopted to decompose the contribution of each part. At the regional level, this paper decomposes the decoupling index into eight influencing factors, and employs Geographically Temporally Weighted Regression (GTWR) to investigate the spatial and temporal heterogeneity of the influencing factors in each region. The following conclusions are generated: (1) At the national level, decoupling of carbon emissions from GDP consists of weak decoupling and expansive coupling. (2) At the national level, the decoupling effect of carbon emissions from fossil energy is an important negative driver for index changes of carbon emissions decoupled from GDP. The decoupling effect of total energy consumption from GDP is an important positive driver for index changes of carbon emissions decoupled from GDP. However, the decoupling effect of fossil energy from total energy consumption is a minimal positive driver. (3) At the regional level, decoupling of carbon emissions from GDP consists of weak decoupling, expansive coupling, and expansive negative decoupling in most years. (4) At the regional level, each influencing factor shows spatial and temporal heterogeneity based on GTWR. In terms of policy implications, central and western regions should increase the degree of openness to the outside world and strengthen the rectification of high-pollution and high-emission enterprises. Meanwhile, it's important to accelerate the industrialisation process and reduce excessive dependence on fossil fuels such as coal. Unlabelled Image • Decomposition analysis, SE-SBM and GTWR are employed. • Decoupling consists of weak decoupling and expansive coupling at national level. • C-FE decoupling effect is an important negative driver for index changes. • The same influencing factors have significant differences in different regions. [ABSTRACT FROM AUTHOR]

Metals remain essential structural materials for many demanding engineering applications requiring high strength at elevated temperatures and good performance in environments subjected to high thermal fluctuations such as often encountered in the automotive, rail and aircraft industries. The sand-casting process is one of the most preferred methods of producing complex and intricate shaped components out of metals with good strength at elevated temperatures. Unfortunately, the sand casting process also leads to the direct and indirect production of carbon dioxide. Three-dimensional sand mold printing has been revolutionizing traditional production methods by reducing the unnecessary consumption of metal and chemicals when manufacturing a part through sand casting. This paper explores the opportunities that are emerging in the area of 3D printing of sand molds and the positive impact that these new technologies and practices are having on the environmental impact of current sand-casting processes. The paper demonstrates that 3D printing of sand molds enables new manufacturing strategies reducing the direct CO 2 emissions and reducing the amount of metal required by enabling design optimization of both the component and mold/core assembly. Further benefits will be realized through the development of environmentally friendly binder systems. [ABSTRACT FROM AUTHOR]

Abstract The energy-saving and emission reduction (ESER) strategy is a crucial measure for promoting the sustainability of manufacturing industry in green transition. Analyzing current practices and limitations of the ESER in the manufacturing industry, this paper proposes a new concept entitled lean energy-saving and emission-reduction (LESER) and an approach to effectively improve the energy efficiency and reduce waste emissions. This paper illustrates the definition of the LESER and establishes an implementation framework for LESER to improve the manufacturing process. To quantify and evaluate performance of LESER, the state space model of the carbon footprint for energy consumption and waste discharge is established. A method for implementing the LESER strategy is constructed in the following steps: (i) clarification of the current situation; (ii) analysis of the root cause; (iii) improvement; (iv) evaluation of the carbon emissions; (v) sustaining and standardizing. Finally, the LESER strategy is applied to the Zcrubber Group Co. Ltd., which is characterized with high pollution, high energy consumption, and high emission in green transition. Results demonstrate practicability of the proposed strategy to offer an effective measure for promoting sustainability of manufacturing industry. Graphical abstract Unlabelled Image Highlights • Proposing the LESER for promoting sustainability of manufacturing industry • Illustrating the concept and connotation of the LESER • Describing the carbon footprint for manufacturing process by state space model • Presenting the implementation processes of the LESER [ABSTRACT FROM AUTHOR]

Abstract To deal with climate change, clean energy is a necessity in the present world. Energy requirement is growing faster each decade. Over the long term some of the traditional sources (coal, gas and oil) have become inadequate to meet up the increasing demand. The current consumption rate of fossil fuel will make them extinct by the year 2050 to 2100. Based on these facts nuclear power plant is a strategic choice to develop a clean energy. This paper is an outcome of the review - Nuclear power as foundation of a clean energy future. Though nuclear energy can't be called as 'Carbon neutral' but it gives rise to much less emission of carbon dioxide than fossil fuels. Comparing with other energy structures, nuclear electricity chain emits a limited amount of greenhouse gas. Despite the uncertainty of building nuclear power plants in the future, this paper will further discuss climate changing issues have larger impact than the policies which are against the nuclear electricity production. The safety issue which is a public concern is also discussed here in short. [ABSTRACT FROM AUTHOR]

Abstract Cryptocurrency is a relatively new combination of cryptology and currency in financial areas and is increasingly frequently used worldwide. Blockchain applications are expected to reshape the renewable energy market. However, there is a lack of studies covering the power usage of digital currencies. Therefore, this study ran experiments on mining efficiency of nine kinds of cryptocurrencies and ten algorithms. A comparison of statistical analysis of data in a benchmark and experiment results of Monero mining was conducted. Thereafter, this study provided an estimation of global electricity consumption of the Monero mining activity. The results indicated that the hashing algorithm mainly determines the mining efficiency. Data analysis and experiments and estimated Monero mining electricity consumption in the world and its carbon emission in China as a case study. In 2018, Monero mining may consume 645.62 GWh of electricity in the world after its hard fork. The Monero mining in China may consume 30.34 GWh and contribute a carbon emission of 19.12–19.42 thousand tons from April to December in 2018. Although cryptocurrency mining and blockchain technology are promising, their influence on energy conversation and sustainable development should be further studied. Highlights • This paper presented digital coin mining other than Bitcoin. • Data analysis and experiments on Monero mining were conducted as a case study. • Results showed electricity consumption of Monero mining globally. • The paper discussed the potentials of blockchain in the energy industry. • The results could be used when measuring the efficiency of the blockchain. [ABSTRACT FROM AUTHOR]

Highlights • This paper proposes a fixed-sum input DEA model under energy shortage constraint. • This paper proposes an energy consumption structure measurement model. • The adjustment index is proposed for optimizing the energy consumption structure. • Empirical results show that energy configuration of China's provinces is inefficient. • All provinces currently have unreasonable energy consumption structures. Abstract As the world's main energy consumer, China faces an increasingly prominent conflict between its growing energy consumption and its unreasonable energy consumption structure. To optimize China's energy consumption structure, while simultaneously controlling its carbon emissions, this study proposes two models for the analysis of China's energy configuration and energy consumption structure based on the data envelopment analysis (DEA) method. A fixed-sum input DEA model is proposed with which the energy input frontier is determined under consideration of China's energy shortage constraint. Furthermore, an energy consumption structure measurement model is developed to determine the adjustment direction and potential of the current energy consumption structure. The proposed models are further applied for the practical energy consumption structure evaluation in Chinese provinces. The obtained results show that almost all provinces suffer from an inefficient energy configuration and Eastern China experiences severe energy shortage. In addition, all provinces currently have unreasonable energy consumption structures. In particular, the proportion of gas consumption to total energy usage should be increased in all provinces. Furthermore, Chinese provinces with inefficient energy structures exhibit the geographical feature. The coal adjustment ratios in the central and western regions account for 70% of the total, which is much higher than the coal adjustment rate of the eastern region (50%). Based on these results, policies are also suggested to adjust China's energy consumption structure, such as reducing high carbon energy, implementing energy price reform, carbon tax, and clean energy subsidy. [ABSTRACT FROM AUTHOR]

Carbon emissions from the combustion of fossil fuels continue to be a significant barrier in the fight against global climate change. Carbon taxes are an energy policy tool for substantially carbon dioxide (CO 2) emissions reduction. This paper proposes a new strategy to promote the use of renewable energy by enterprises in industrial parks against the backdrop of a carbon tax. Using an evolutionary game model based on life cycle assessment (LCA), this study investigates the impact of dynamic carbon tax mechanisms, fines, and subsidies on the energy choices of industrial park enterprises. This paper's primary contribution is the incorporation of LCA into the evolutionary game model, which facilitates accurate calculation of the carbon tax parameter value. According to the study's findings, the carbon tax rate increases the propensity of enterprises to acquire a high percentage of renewable energy. While there is a negative relationship between fines and the propensity of enterprises to adopt a low proportion of renewable energy, an increase in subsidies decreases the proportion of enterprises that purchase a high proportion of renewable energy. This paper concludes by providing a theoretical foundation for policymakers to construct precise carbon tax schemes and promote renewable energy consumption among industrial park enterprises while simultaneously reducing carbon emissions. [Display omitted] • Carbon tax & energy decisions: life cycle assessment & evolutionary game theory. • Life cycle assessment for enterprise carbon taxes under multiple energy options. • Carbon tax mechanisms, fines, and subsidies influence enterprises energy decisions. • Excessive subsidies discourage renewable energy use, but fines can. [ABSTRACT FROM AUTHOR]

Traffic oscillation, a traffic phenomenon that generates many carbon emissions, originates from the amplification of traffic disturbance. The connected and automated vehicle (CAV) technology may help to weaken oscillations and reduce carbon emissions from mixed traffic. This paper explores the effects of various characteristics of disturbances and mixed platoons on CO 2 emissions under different disturbance types. The results indicate that the addition of even only one CAV reduces carbon emissions under some disturbances. The disturbance types that make mixed traffic increase carbon emissions have also been identified. Diverse disturbance characteristics play different essential roles in ecological impacts of CAVs. The platoon characteristics such as CAV's position in a platoon and the heterogeneity of human factors, affect the extent of carbon emission reduction. Based on these findings, this paper proposes optimization recommendations for CAV cooperative eco-driving and green traffic management. [ABSTRACT FROM AUTHOR]

Multi-layer networks could reveal the carbon emission structure of urban traffic formed after residents choose the means and purpose of trips. In this paper, a multi-layer network model was proposed and the carbon emission characteristics of urban trips were analyzed. In addition, the carbon reduction potential assessment methods based on nodes and edge feature indexes to identify the carbon reduction areas of the trip network. An empirical study was carried out on Shenzhen and the results showed that: 1) The carbon emission in Shenzhen is unbalanced in spatial and is dense in the west and sparse in the east, but the carbon emission of different networks shows a similar fluctuation trend over time; 2) multi-layer network represents community structure, while communities of "residence-enterprise" network and "residence-park" network are internally closely connected; 3) the carbon emission reduction potential of residential nodes is low in the west and high in the east. The mode of the urban trip and the law of geographical space connection expressed by it were understood by establishing a multi-layer network embedded in geographic space in this paper. The conclusions hereof are of supportive significance for the formulation of space emission reduction policies. • The multi-layer networks that characterize residents' diversified trips was establish. • Carbon emission estimation method considering parameters of road type and speed class was proposed. • The correlation between residents' trips modes and carbon emissions was depicted. • The carbon emission network for residents' trips present community structure and spatial variation. • Differentiated zoning strategies were discussed based on carbon emissions and reduction potential. [ABSTRACT FROM AUTHOR]

Sustainability pertains to the interplay between environmental, economic, and social systems at varying scales. In transportation systems, vehicles use different fuels to fulfill their energy requirements. However these fuels emit green house gases that pollute the environment. Taking into account this fact, the present paper delves into the investigation of a multi-objective multi-item fixed cost solid transportation problem with various driving styles. Also, three types of fuels are incorporated in the proposed model. The primary focus of this paper is to maximize profit by reducing breakage of the items along with reducing carbon emission and time of transportation. In real-life situations, the parameters of multi-objective multi-item fixed cost solid transportation problem may not be tackled crisply. Hence triangular intuitionistic fuzzy numbers are introduced in the proposed model. The score function is employed to convert intuitionistic fuzzy problem to deterministic one. Thereafter, a methodology is developed to solve the proposed model involving different driving styles and is extended to consider the optimistic, pessimistic, and mixed view of the decision maker. Comparisons are made among the compromise solutions that are derived from different approaches and are shown by various charts and graphs. A case study is included to illustrate the applicability and validity of the proposed methodology. • A sustainable multi-objective 4DTP with multiple items and fixed charge is modeled. • Three types of fuels: diesel, petrol and CNG are incorporated in the proposed model. • Driving style is included to analyze the fuel consumption and breakability of items. • The decision-maker's optimistic, pessimistic, and mixed viewpoints are discussed. [ABSTRACT FROM AUTHOR]

The maritime industry has entered its pathway of decarbonization. To achieve the IMO's ambitious goals of an absolute emissions reduction of 50% by 2050, and a 70% carbon intensity reduction by 2050 compared to the 2008 level, various options for the adoption of technologies and alternative fuels are considered by the market stakeholders.Ammonia, one of the most promising alternative marine fuels has long been considered to reduce carbon emissions. And solid oxide fuel cell is expected to transform ship propulsion technology in the future due to its high utilization of fuels. In this paper, a feasibility study is performed to assess the application of an ammonia cracker-integrated solid oxide fuel cell (hereafter as Ammonia SOFC) system on an ocean-going vessel through a detailed CAPEX, FuelEX, OPEX, Carbon tax, and carbon emission analysis. Comparison is made with direct ammonia, LNG and conventional fuels fired heat engines. The result concludes that it can be economically viable to apply to deep-sea shipping, compared to other marine fuels and propulsion technologies. • An economic assessment of a containership applying ammonia SOFC system is benchmarked with other propulsion technologies. • CAPEX of the Ammonia SOFC system is higher than other propulsion solutions, while OPEX is comparable. • Ammonia SOFC system has a comparable cumulative cost with LNG fuelled ICE and HFO fuelled ICE propulsion types. • Ammonia SOFC system is economically viable for future marine applications. [ABSTRACT FROM AUTHOR]

Market-based measures, like the carbon cap-and-trade (CCT) scheme, can effectively reduce emissions and promote green technology by internalizing the external costs in the shipping industry. Under the CCT scheme, the government sets the quantity commitment of carbon emissions for a unit of shipping work, and the excessive quantity must be traded from the inner shipping market. To cope with carbon regulation, carriers can adjust their ship deployment plan and adopt new engine technologies to support low-carbon fuels. This paper proposes a bi-level programming model to investigate the effects of the CCT scheme on the decisions of carriers in a competitive inland container shipping network. At the upper level, carriers adjust their own ship deployment plan and the choice of technology and fuel type. At the lower level, the shippers between different origins and destinations choose the shipping lines with the given ship deployment plans of carriers. Two kinds of equilibrium are considered in the proposed problem: the competitive equilibrium of carriers on freight shipment in the inland shipping network and the trading equilibrium of carbon quotas. The former equilibrium can be captured by a traffic assignment model in a similar transit transportation network, and the latter equilibrium follows the idea of the demand–supply equilibrium of carbon quotas in the traditional market. The two equilibria interact since the freight distribution affects the shipping revenue of each ship and the trading equilibrium is related to the total cost of the ship. The properties of the optimal choice of technology and fuel type are theoretically studied. We find that the optimal choice of fuel technology for each ship assigned to any shipping line at any given CCT must be Pareto optimal in the sense of cost-related and emission-relative measures. Based on the result, the optimal solution of the proposed model can be calculated by a sampling-based method. Numerical examples based on the Yangtze River are further adopted to illustrate the proposed model and conclusions. • Ship deployment with fuel-technology choice under CCT scheme is addressed. • Carriers' competition and trading equilibrium of carbon allowances are considered. • Properties of the optimal solution of fuel-technology choice are studied. • A real-world shipping network based on the Yangtze River is investigated. [ABSTRACT FROM AUTHOR]

• Integration of P2G and EVPLs in order to reduce the operating costs of electricity and natural gas networks. • Investigating the effect of integrating P2G system and EVPLs on carbon emissions. • Linearization of nonlinear equations using mathematical methods. Energy is considered one of the most important human needs. Therefore, various energy resources are exploited to meet human needs. Among all available resources, electricity and natural gas have accounted for the largest share in meeting these needs. Accordingly, management of these resources is one of the most important issues in different societies. This paper presents a multi-objective optimization model for optimal management and operation of the power distribution network and natural gas network as well as reducing carbon emissions. In this modeling, the natural gas network and distribution power network are connected through two electric vehicle parking lots (EVPLs) and two gas-fired units (GFUs), and these two EVPLs and GFUs exchange energy with each other. The EVPLs are equipped with various facilities including power-to-gas (P2G) systems and natural gas storage that facilitate energy exchange between the two networks. The modeling presented in this article is based on mixed integer linear programming, so the nonlinear equations including power flow equations, the Weymouth equation and the cost functions of GFUs are also linearized using various methods. It should be noted that the price of purchased electricity poses uncertainty which is modeled using robust optimization techniques. The proposed model is tested on the 33-bus power distribution network and the 20-node natural gas network. The results show that the presence of EVPLs significantly reduces the operational costs of the natural gas network and power distribution network and carbon emissions. [ABSTRACT FROM AUTHOR]

Abstract Capital formation has contributed the most towards Chinas' carbon emissions, but capital assets are ultimately used for production. This paper by using input-output model embeds carbon emissions from capital formation to rest of final demand categories and compares results with other two common methods. Our approach has led to embedding of more carbon emissions and increased intensity. The analysis is mainly based on China's 2012 input-output data and China Statistical Yearbook data. Under new approach, annual carbon consumption of rural households, urban households, government and foreign consumption significantly increased, urban households per capita annual carbon consumption is about 3.26 times of the rural households. Government's 94% carbon consumption is from other non-material production sector. China's total annual carbon export volume increased by48% under new approach. Some study limitations are also discussed in this paper. Our work is of certain reference value for understanding carbon emissions from the perspective of final consumption. [ABSTRACT FROM AUTHOR]

Abstract To alleviate the high risks to oil supply caused by high dependency on oil imports, China is actively seeking alternative energy sources to reduce its imported oil (IO). Coal-to-liquid (CTL) is regarded as an option in China. However, few analyses have examined the net energy impact of substituting IO with CTL and determined how effective the substitution would be. Energy return on investment (EROI) analysis, an important index to characterise the viability of a natural resource from an energy standpoint, can provide insights into these issues. This paper first introduces new methods to calculate the EROIs between CTL and IO from a life-cycle perspective. Then, based on the EROI values, a new, simple method is provided to calculate the net energy change resulting from the substitution of IO with CTL. The results show that without considering the environmental inputs, the mean EROI of CTL with/without internal energy inputs is 0.29/1.44, and that of IO is approximately 5.60. When environmental inputs are considered, these values decrease to 0.27/1.09 and 5.07, respectively. In general, IO has a better net energy return than CTL. Moreover, substituting 1 MJ IO with CTL would lead to a net energy loss of 0.3–4.6 MJ. The above results suggest that China should be more cautious in developing the coal liquefaction industry from a net energy perspective. Highlights • New methods are provided to estimate the EROI of IO and CTL from a life-cycle perspective. • This paper considers the impact of the energy inputs to control GHG emissions on EROI. • The EROIs of IO and CTL are about 0.86–1.79 and 4.28–6.50 respectively. • Substituting 1 MJ IO with CTL would lead to a net energy loss of 0.3–4.6 MJ. [ABSTRACT FROM AUTHOR]

Highlights • A new analytical method and decision-making process are demonstrated in this paper for new town planning. • A “population-economy-land-carbon emission” model for new towns is built based on system dynamics. • In a case study, a quantitative analysis for carbon emission is performed and different planning schemes are compared. • The framework developed for carbon prediction can be applied to other towns in similar climatic zones of China. • Other planning indicators related to each sub-system are highlighted for the interest of further research. Abstract The high contribution of new constructions to rapidly growing carbon emissions renders the planning of new towns a strategic element to consider in building sustainable cities. However, studies on qualitative prediction of carbon emission for new town planning are still deficient. This paper uses system dynamics approach and establishes a “population-economy-land-carbon emission” model to perform, unlike previous models, quantitative predictions on new towns, sector by sector. The prediction model which is applied to Xishan new town (Urumqi) can be used to select the best planning scenario based on the predictions of carbon emission and land-use economic efficiency. The model can also help determine the key sectors—the industrial sector in this case—to consider in carbon regulation strategies along with the economic targets. The model applied to Xishan reveals also that considering the building sector, renewable energies should be promoted, especially for heating. Transportation systems should be operated based on the population density to avoid more pollution than necessary. Finally, the study shows that cities must rely on a larger, perhaps even regional, the scale of green spaces to be meet carbon sequestration challenges. The standard framework used here can be considered for other towns in similar climatic zones of China. [ABSTRACT FROM AUTHOR]

By positioning in the discourse that economic output is always coupled with natural resource depletion, pollution, and carbon emission, decoupling analysis is widely adopted to evaluate how “quality” economic growth can lead to fewer such downsides so as to encourage sustainable development. This paper aims at examining the decoupling relationship between economic output and carbon emission by focusing on China's construction industry, which is a pillar industry for national economic growth, meanwhile contributes a huge amount of carbon emission. The method of Tapio decoupling model is used to examine the decoupling relationships at both national and provincial levels from 2005 to 2015. It continues to identify the driving force leading to a certain decoupling state by adopting the logarithmic mean Divisia index (LMDI). Results show that: (1) there existed an expansive decoupling relationship between economic growth and construction carbon emission in most provinces of China during 2005–2015; (2) Shanghai presented the best decoupling performance, while in contrast, other provinces such as Guizhou and Fujian displayed expansive negative decoupling state; and (3) “Economic output” played the most significant role in inhibiting the decoupling at both national and provincial levels, while “Indirect carbon intensity” was the main driver for promoting the national decoupling. Although the paper refers to the specific construction of China, the decoupling analysis approach can be extended to other countries as well as to other pollutants such as land pollution, waste water and haze. The understanding of driving forces for the decoupling state in China's construction industry provides international policy-makers with valuable reference for formulating effective measures to balance the dilemma between economic output and carbon emission. [ABSTRACT FROM AUTHOR]

Carbon emissions from the tourism industry are an important measure of the impact tourism has on the environment. Previous studies are predominantly focused on the static estimation of carbon emission from tourism transport. The effective estimation and analysis of carbon emission flows from self-driving tourism, and it’s related determinants, has become increasingly important. Using expressway traffic flow data at the level of toll-gate across Jiangsu Province in China (2014), this paper has estimated the carbon emission flows from self-driving tourism between counties, analyzed the spatial patterns of its inflow, outflow and net flows, and modelled the determinants of these flows globally and locally using the geographically weighted regression method. The spatial distribution of these flows show high concentration in the South, gradually decreasing to the North. The two geographically weighted regression models demonstrate that the determinants of both inflows (the per capita gross domestic product. and the scenic spot’s score) and outflows (the per capita and total population of permanent residents) indicate spatial non-stationarity across Jiangsu province. The flow perspective and geographically weighted regression methods used in this paper have been proven to be effective in theoretical understanding and methodogical analysis of carbon emission trading. It is concluded that the spatial variation of these determinants has provided important evidence for carbon emission trading at county level. This suggests that local governments should take the variations of per capita gross domestic product, score of attractive spots and total population of permanent residents into the process of estimating carbon emission trading between counties. [ABSTRACT FROM AUTHOR]

This study explores how an institutional innovation in the 20th Century – Coasean bargaining - could contribute to build a low carbon China in the 21st Century. China has surpassed all nations and become the world's largest emitter of CO 2 since 2007. A time-series analysis has revealed that crude oil and coal consumption, inter alia, are the primary factors contributing to the variations in CO 2 emissions in China. To honour China's commitments in the Paris Agreement 2015, this paper, with reference to the empirical results, estimates the impacts on various sources of energy consumption before 2030. Developed upon the Coasean bargaining framework (1960), the cap-and-trade (CAT) markets have contributed to reduce the pollutants and greenhouse gas emissions in the US and EU. This paper will illustrate how the CAT market can be complementary to administrative measures for CO 2 reduction in China. It will also discuss the institutional settings and constraints of the 7 pilot CAT markets in China, and also the national one to be rolled out in 2017. [ABSTRACT FROM AUTHOR]

Food deterioration is becoming a crucial problem for most countries in the world, which may cause both economical losses and environmental damages. In this paper, a Stackelberg gaming model for a three-level food supply chain (consists of one retailer, one vendor and one supplier) with production disruption is established, which aims to study the optimal pricing, inventory and preservation decisions that maximize the individual profit. In the decentralized supply chain, upstream firms act as leaders and downstream firms as followers. Due to the mathematical complexity, an illustrative algorithm is developed to solve the problem. Numerical tests show that retailer's preservation investment not only benefits itself, but also benefits the vendor and the supplier. Comparing the optimal decisions to that in the ‘forward integration’ and ‘backward integration’ model, supply chain members' vertical cooperation helps to enhance the total profit. Meanwhile, the carbon footprint of the food supply chain is also studied. It is found that, vertical cooperation contributes to the reduction of carbon emission. In most situations, ‘forward integration’ outperforms ‘backward integration’ strategy because it incents the retailer to invest more in preservation and reduce food deterioration. Other managerial implications are also shown in the paper. [ABSTRACT FROM AUTHOR]

Emission Trading Scheme (ETS) has dual aims to reduce emission production and stimulate adoption of long-term abatement technology. Whilst it has generally achieved its first aim, its issues are hindering the accomplishment of the second. Several solutions have been proposed to improve ETS’s efficacy, yet none of them have considered the advancement of Industry 4.0. This paper proposes a novel ETS model customised for Industry 4.0 integration. It incorporates blockchain technology to address ETS’s management and fraud issues whilst it utilizes a reputation system in a new approach to improve ETS efficacy. Specific design of how the blockchain technology and reputation system are used to achieve these objectives is showed within this paper. The case study demonstrates the inner working of reputation-based trading system—in which reputation signifies participants performance and commitment toward emission reduction effort. Multi-criteria analysis is used to evaluate the proposed scheme against conventional ETS model. The result shows that the proposed model is a feasible scheme and that the benefits of its implementation will outweigh its drawback. [ABSTRACT FROM AUTHOR]